{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 以实际工作数据分析探索及分析角度出发，探索DataFrame自带画图功能，出简单数据探索或分析的结论是够用了，如若寻求动态或者更美观的数据展示使用，可以使用pyechart,seaborn等包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import requests\n",
    "import json\n",
    "from fake_useragent import UserAgent\n",
    "import time\n",
    "import matplotlib\n",
    "\n",
    "#图像显示中文\n",
    "%matplotlib inline\n",
    "matplotlib.rcParams['font.sans-serif'] = ['SimHei']\n",
    "matplotlib.rcParams['font.family']='sans-serif'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 爬虫下面段参考出处：https://mp.weixin.qq.com/s/q4_lq0_aHahzuB7yhYTFng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获取一页\n",
    "def get_one_page(page_num):\n",
    "    # 获取URL\n",
    "    url = 'https://api.eol.cn/gkcx/api/'\n",
    "\n",
    "    # 构造headers\n",
    "    headers = {\n",
    "        'User-Agent':\n",
    "        UserAgent().random,\n",
    "        'Origin':\n",
    "        'https://gkcx.eol.cn',\n",
    "        'Referer':\n",
    "        'https://gkcx.eol.cn/school/search?province=&schoolflag=&recomschprop=',\n",
    "    }\n",
    "\n",
    "    # 构造data\n",
    "    data = {\n",
    "        'access_token': \"\",\n",
    "        'admissions': \"\",\n",
    "        'central': \"\",\n",
    "        'department': \"\",\n",
    "        'dual_class': \"\",\n",
    "        'f211': \"\",\n",
    "        'f985': \"\",\n",
    "        'is_dual_class': \"\",\n",
    "        'keyword': \"\",\n",
    "        'page': page_num,\n",
    "        'province_id': \"\",\n",
    "        'request_type': 1,\n",
    "        'school_type': \"\",\n",
    "        'size': 20,\n",
    "        'sort': \"view_total\",\n",
    "        'type': \"\",\n",
    "        'uri': \"apigkcx/api/school/hotlists\",\n",
    "    }\n",
    "\n",
    "    # 发起请求\n",
    "    try:\n",
    "        response = requests.post(url=url, data=data, headers=headers)\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "        time.sleep(3)\n",
    "        response = requests.post(url=url, data=data, headers=headers)\n",
    "\n",
    "    # 解析获取数据\n",
    "    school_data = json.loads(response.text)['data']['item']\n",
    "\n",
    "    # 学校名\n",
    "    school_name = [i.get('name') for i in school_data]\n",
    "    # 隶属部门\n",
    "    belong = [i.get('belong') for i in school_data]\n",
    "    # 高校层次\n",
    "    dual_class_name = [i.get('dual_class_name') for i in school_data]\n",
    "    # 是否985\n",
    "    f985 = [i.get('f985') for i in school_data]\n",
    "    # 是否211\n",
    "    f211 = [i.get('f211') for i in school_data]\n",
    "    # 办学类型\n",
    "    level_name = [i.get('level_name') for i in school_data]\n",
    "    # 院校类型\n",
    "    type_name = [i.get('type_name') for i in school_data]\n",
    "    # 是否公办\n",
    "    nature_name = [i.get('nature_name') for i in school_data]\n",
    "    # 人气值\n",
    "    view_total = [i.get('view_total') for i in school_data]\n",
    "    # 省份\n",
    "    province_name = [i.get('province_name') for i in school_data]\n",
    "    # 城市\n",
    "    city_name = [i.get('city_name') for i in school_data]\n",
    "    # 区域\n",
    "    county_name = [i.get('county_name') for i in school_data]\n",
    "\n",
    "    # 保存数据\n",
    "    df_one = pd.DataFrame({\n",
    "        'school_name': school_name,\n",
    "        'belong': belong,\n",
    "        'dual_class_name': dual_class_name,\n",
    "        'f985': f985,\n",
    "        'f211': f211,\n",
    "        'level_name': level_name,\n",
    "        'type_name': type_name,\n",
    "        'nature_name': nature_name,\n",
    "        'view_total': view_total,\n",
    "        'province_name': province_name,\n",
    "        'city_name': city_name,\n",
    "        'county_name': county_name,\n",
    "    })\n",
    "\n",
    "    return df_one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获取多页\n",
    "def get_all_page(all_page_num):\n",
    "    # 存储表\n",
    "    df_all = pd.DataFrame()\n",
    "\n",
    "    # 循环页数\n",
    "    for i in range(all_page_num):\n",
    "        # 打印进度\n",
    "        #print(f'正在获取第{i + 1}页的高校信息')\n",
    "        # 调用函数\n",
    "        df_one = get_one_page(page_num=i+1)\n",
    "        # 追加\n",
    "        df_all = df_all.append(df_one, ignore_index=True)\n",
    "        # 随机休眠\n",
    "        time.sleep(np.random.uniform(2))\n",
    "\n",
    "    return df_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "if __name__ == '__main__':\n",
    "    # 运行函数\n",
    "    df = get_all_page(all_page_num=148) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school_name</th>\n",
       "      <th>belong</th>\n",
       "      <th>dual_class_name</th>\n",
       "      <th>f985</th>\n",
       "      <th>f211</th>\n",
       "      <th>level_name</th>\n",
       "      <th>type_name</th>\n",
       "      <th>nature_name</th>\n",
       "      <th>view_total</th>\n",
       "      <th>province_name</th>\n",
       "      <th>city_name</th>\n",
       "      <th>county_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>河南大学</td>\n",
       "      <td>河南省</td>\n",
       "      <td>双一流</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>普通本科</td>\n",
       "      <td>综合类</td>\n",
       "      <td>公办</td>\n",
       "      <td>996.1w</td>\n",
       "      <td>河南</td>\n",
       "      <td>开封市</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>上海对外经贸大学</td>\n",
       "      <td>上海市</td>\n",
       "      <td></td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>普通本科</td>\n",
       "      <td>财经类</td>\n",
       "      <td>公办</td>\n",
       "      <td>427.4w</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海市</td>\n",
       "      <td>松江区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>重庆邮电大学</td>\n",
       "      <td>重庆市</td>\n",
       "      <td></td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>普通本科</td>\n",
       "      <td>理工类</td>\n",
       "      <td>公办</td>\n",
       "      <td>720w</td>\n",
       "      <td>重庆</td>\n",
       "      <td>重庆市</td>\n",
       "      <td>南岸区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>贵州师范大学</td>\n",
       "      <td>贵州省</td>\n",
       "      <td></td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>普通本科</td>\n",
       "      <td>师范类</td>\n",
       "      <td>公办</td>\n",
       "      <td>357w</td>\n",
       "      <td>贵州</td>\n",
       "      <td>贵阳市</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>重庆理工大学</td>\n",
       "      <td>重庆市</td>\n",
       "      <td></td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>普通本科</td>\n",
       "      <td>理工类</td>\n",
       "      <td>公办</td>\n",
       "      <td>805.9w</td>\n",
       "      <td>重庆</td>\n",
       "      <td>重庆市</td>\n",
       "      <td>巴南区</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  school_name belong dual_class_name  f985  f211 level_name type_name  \\\n",
       "0        河南大学    河南省             双一流   2.0   2.0       普通本科       综合类   \n",
       "1    上海对外经贸大学    上海市                   2.0   2.0       普通本科       财经类   \n",
       "2      重庆邮电大学    重庆市                   2.0   2.0       普通本科       理工类   \n",
       "3      贵州师范大学    贵州省                   2.0   2.0       普通本科       师范类   \n",
       "4      重庆理工大学    重庆市                   2.0   2.0       普通本科       理工类   \n",
       "\n",
       "  nature_name view_total province_name city_name county_name  \n",
       "0          公办     996.1w            河南       开封市              \n",
       "1          公办     427.4w            上海       上海市         松江区  \n",
       "2          公办       720w            重庆       重庆市         南岸区  \n",
       "3          公办       357w            贵州       贵阳市              \n",
       "4          公办     805.9w            重庆       重庆市         巴南区  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据处理 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "level_name\n",
       "             1\n",
       "专科（高职）    1520\n",
       "普通本科      1419\n",
       "Name: level_name, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#level_name字段 探索\n",
    "df['level_name'].groupby(df['level_name']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school_name</th>\n",
       "      <th>belong</th>\n",
       "      <th>dual_class_name</th>\n",
       "      <th>f985</th>\n",
       "      <th>f211</th>\n",
       "      <th>level_name</th>\n",
       "      <th>type_name</th>\n",
       "      <th>nature_name</th>\n",
       "      <th>view_total</th>\n",
       "      <th>province_name</th>\n",
       "      <th>city_name</th>\n",
       "      <th>county_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2905</th>\n",
       "      <td>中国人民解放军海军士官学校</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1058</td>\n",
       "      <td>安徽</td>\n",
       "      <td>蚌埠市</td>\n",
       "      <td>蚌埠市经济开发区</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        school_name belong dual_class_name  f985  f211 level_name type_name  \\\n",
       "2905  中国人民解放军海军士官学校                          2.0   2.0                        \n",
       "\n",
       "     nature_name view_total province_name city_name county_name  \n",
       "2905                   1058            安徽       蚌埠市    蚌埠市经济开发区  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#某士官学校 level为空， 经查询后填充\n",
    "df[(df['level_name']!='专科（高职）')&(df['level_name']!='普通本科')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[2905,'level_name'] = '专科（高职）'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#view_total字段\n",
    "#因为同时有带单位w及没有带单位的，故判断处理，遍历每一行\n",
    "for index, row in df.iterrows():\n",
    "    if df.loc[index,'view_total'].find('w')>0:\n",
    "        df.loc[index,'view_total'] = float(df.loc[index,'view_total'].replace('w',''))*10000\n",
    "    else:\n",
    "        df.loc[index,'view_total'] = float(df.loc[index,'view_total'])\n",
    "\n",
    "#数据量太大时候上面遍历方式会慢，可以参考使用下面方式\n",
    "#df['view_num'] = df.view_total.str.extract(r'(\\d+.*\\d+)').astype('float')\n",
    "#df['unit'] = df.view_total.str.extract(r'(w)').replace({'w':10000, np.nan:1})\n",
    "#df['view_total'] = df['view_num'] * df['unit']\n",
    "#df.drop(['view_num', 'unit'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#禁用科学计数法  方便界面查看具体数值\n",
    "np.set_printoptions(suppress=True,   precision=10,  threshold=2000,  linewidth=150)  \n",
    "pd.set_option('display.float_format',lambda x : '%.2f' % x)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "#参数说明\n",
    "DataFrame.plot(x=None, y=None, kind='line', ax=None, subplots=False, \n",
    "                sharex=None, sharey=False, layout=None, figsize=None, \n",
    "                use_index=True, title=None, grid=None, legend=True, \n",
    "                style=None, logx=False, logy=False, loglog=False, \n",
    "                xticks=None, yticks=None, xlim=None, ylim=None, rot=None, \n",
    "                fontsize=None, colormap=None, position=0.5, table=False, yerr=None, \n",
    "                xerr=None, stacked=True/False, sort_columns=False, \n",
    "                secondary_y=False, mark_right=True, **kwds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 柱状图：各省学校分布 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x6bb0c18>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JczkSKkmSJEnqjEmoJEmSJKkzJqGSJEmSpM6YhEqSJEmSOmMSKkmSJEnqjEmoJEmSJKkzJqGSJEmSpM6YhEqSJEmSOjNxQW+AJEmShmfqzFl9vW7OMTOGvCWSNG+OhEqSJEmSOmMSKkmSJEnqjEmoJEmSJKkzJqGSJEmSpM64MJEkSZL64iJIkvrhSKgkSZIkqTMmoZIkSZKkzpiESpIkSZI6YxIqSZIkSeqMCxNJkiTpAc9FkKRFhyOhkiRJkqTOmIRKkiRJkjpjEipJkiRJ6oxJqCRJkiSpMyahkiRJkqTOuDquJEmSNIIr8Urjy5FQSZIkSVJnTEIlSZIkSZ0xCZUkSZIkdcYkVJIkSZLUGZNQSZIkSVJnTEIlSZIkSZ0xCZUkSZIkdWbM3xMaEROBX7d/AK8CDgLWB2Zl5tuHt3mSJEnSosvvJNWDUT8joRsA52bm9MycDqwNTMjMzYE1I2LtYW6gJEmSJGnRMeaRUGAzYOeI2Bq4Frgb+ET724XAFsAvRr8oIvYD9gNYffXV+9pYSZIkSdLCrZ+R0O8A22bmJsAkYEfg+va3PwMrz+tFmXlqZk7LzGlTpkzpa2MlSZIkSQu3fpLQazLzhvbzbGBFYMn2+9J9vqckSZIk6UGgn4Tx7IjYMCImALsAB1BTcAE2BOYMadskSZIkSYuYfu4JPRI4Bwjgc8BngMsjYhVqau5mw9s8SZIkSdKiZMxJaGb+kFoh998iYjqwHXBsZt42nE2TJEmSJC1q+hkJ/S+ZeStzV8iVJEmSJGmeXERIkiRJktQZk1BJkiRJUmdMQiVJkiRJnTEJlSRJkiR1xiRUkiRJktQZk1BJkiRJUmdMQiVJkiRJnTEJlSRJkiR1xiRUkiRJktQZk1BJkiRJUmdMQiVJkiRJnTEJlSRJkiR1xiRUkiRJktQZk1BJkiRJUmdMQiVJkiRJnTEJlSRJkiR1xiRUkiRJktQZk1BJkiRJUmdMQiVJkiRJnTEJlSRJkiR1xiRUkiRJktQZk1BJkiRJUmdMQiVJkiRJnTEJlSRJkiR1xiRUkiRJktQZk1BJkiRJUmdMQiVJkiRJnTEJlSRJkiR1xiRUkiRJktQZk1BJkiRJUmdMQiVJkiRJnTEJlSRJkiR1xiRUkiRJktQZk1BJkiRJUmcm9vOiiFgWOA+YANwB7Ab8Evh1e8qrMvPaoWyhJEmSJGmR0e9I6J7A8Zm5PfBHYCZwbmZOb/9MQCVJkiRJ/6WvJDQz35+ZF7VfpwD3ADtHxFURcXpE9DXCKkmSJElatA10T2hEbA4sB1wEbJuZmwCTgJ3m8dz9ImJ2RMy+6aabBgkrSZIkSVpI9Z2ERsTywEnAPsA1mXlD+9NsYO3Rz8/MUzNzWmZOmzJlSr9hJUmSJEkLsb6S0IhYHDgfOCQzfwucHREbRsQEYBfgB0PcRkmSJEnSIqLfkdB9gY2BN0fEJcCPgLOB7wNXZObFw9k8SZIkSdKipK8FhDLzFOCUUQ8fMfjmSJIkSZIWZQMtTCRJkiRJ0liYhEqSJEmSOuP3eUqSJEkPAlNnzurrdXOOmTHkLdGDnSOhkiRJkqTOOBIqSZIkaagcddX/4kioJEmSJKkzJqGSJEmSpM6YhEqSJEmSOmMSKkmSJEnqjEmoJEmSJKkzJqGSJEmSpM6YhEqSJEmSOmMSKkmSJEnqjEmoJEmSJKkzJqGSJEmSpM6YhEqSJEmSOjNxQW+AJEmSJA1i6sxZY37NnGNmjMOWaH44EipJkiRJ6owjoZIkSZI0nxx1HZwjoZIkSZKkzpiESpIkSZI6YxIqSZIkSeqMSagkSZIkqTMmoZIkSZKkzpiESpIkSZI6YxIqSZIkSeqMSagkSZIkqTMmoZIkSZKkzkxc0BsgSZIkSfpvU2fOGvNr5hwzYxy2ZLgcCZUkSZIkdcaRUEmSJEl6kOty1NWRUEmSJElSZ0xCJUmSJEmdMQmVJEmSJHXGJFSSJEmS1BmTUEmSJElSZ4aahEbE6RFxRUS8ZZjvK0mSJElaNAwtCY2IXYEJmbk5sGZErD2s95YkSZIkLRqGORI6HfhE+/lCYIshvrckSZIkaREQmTmcN4o4HXhvZv4gIrYHNs7MY0b8fT9gv/brOsDP+gizInDzwBtrLGMZy1jGMlb38YxlLGMZy1jGejDFelRmTpnXHyYOtj3/4W/Aku3npRk1ypqZpwKnDhIgImZn5rRB3sNYxjKWsYxlrAURz1jGMpaxjGUsY5VhTse9mrlTcDcE5gzxvSVJkiRJi4BhjoR+Brg8IlYBdgQ2G+J7S5IkSZIWAUMbCc3M26nFib4NbJ2Ztw3rvUcYaDqvsYxlLGMZy1gLMJ6xjGUsYxnLWMZiiAsTSZIkSZJ0f4Z5T6gkSZIkSf+TSagkSZIkqTMLXRIaEbGgt0FjFxGLL+ht0PzzeA1HV+VVRCx5/88aWizPjQF1ebxavEXymEXEQteGUXc8PwbTZbkRERM6jLVInhcRsXTH8Qb+HBe6A5GZGRGLR8TzImLZLmJ2fcKOZ8O1994dX/A7AetHxITxbpRHxF4R8bDxjDEiVpef4aJ6vHrn47heYyPiTOmi3IiIwyNi6ezgpvuIOALYfLzjtFi7MveruBYpETG1i06DLo9Xi9fZ9TyP2A8dp/d9bUSslJn/6rCMWr6j86PLsn6tiFhtUevcj4iXRcSy7fzouv22WPt/Updxh63jdsA+wNrjGWOUTs6JLq+viHgV8OTxjtNiHRURjxzG9bVQJaER8aSIeBLwIWCdcVqBd3TMRwLrRsRDxjFGr5J7BFSiPV5xWhI/HZjZUeGyE7AycCewZIs/Ludd6wV6DHB4RCwzHjFGxFoqM+9tn+H6i1CseR2vcWkURcRewEvar7tFxJLjdW60/dgO+CiwV0QsNx5xACLiRGDdzPzbeMUYEesY4DGZ+bURj43LNR0RuwHP7SjWwyJi04jYNyI2G++GeUS8j9q3ce006PJ4tffurPwdEbNXn+0CfDIiHjIO+zgZ+GBELN/2adw+w/b+OwDvB8a1AysiHt3K+qUi4skRMcyv0Rsd6zHAE4CnAEuNY5xJEbF+RLw9IvaLiIeO5zkYEQcArwdeGxHLdNFRMVKLtzhwVESsN4z3HHFNbRgRz4iIp0bEeB6zLtsBuwLbAF3Ul9tGxLtoq7xGxORxjNXJ9dViHQM8Ddg2ItYdz/oyIo4CXgicHRErDpqILhRJ6IgC5NHAN4GbM/NtHcR9JHUCbQmMW0XXLvCnAe/tJaIt/lDiRVmjxdkGOBK4LDPvHceEd9OoHv9lqWN2FPCpiJgyXpVCa/S/HbgOOHK8EtGI2B54QdSI65OZm0gttLHu53jdO06NhjnAlhHxPeBg6lIY6rkREZMj4gkRsRXwJuBi4Jr2tyWGFWdEvJOA5wOHtN/HrTc8Ig4H1gFe1H5/QkQsOR6N8tZQ2L2jWDtQ1/EbgJ2A7TPz3mHGGBXvHcBSmfmuUY8Pe78Op7vj1Xn52+L2Ojq3Bw4HXp+Zfx92PZOZ7wC+Bnw4IlYYp89w2Yg4MCI2B/YHTsrMv4xjY3xT4OKI2Bj4APCUzLwnIhYbZvkb1cEzHViTSuanA1tHxMox5JHriNiZKg+fB+xFNV7/MV7nYNRo0CuAW4CVgENbO2DcR5db4tZzHnBnZv5kSG+/UkQ8ETiXqlt2B5ZpcYdZX3baDojq2HwhMA14ZUQ8apjvPw/rADsAV0bEZsBmww7Q5fXV4p0EPB14MxDApsDi7W/DLhNPAFbMzNWAT1CdjL06pa9zY6FIQoHVWq/P5sDJwCoRcUDr1dg+Ih4TQ7rHphX4a7TCcx1geeCezLxzmL3ILTHcov38ROAw4MTMvCFa78wQK+5lgDdExFuBmcAhmXl51DSBfSJi4jhUCNsCa1GJxhuAHwBnAmeNaDQMs2LdLCLemZn/AN4L/IFxSESjOgnuASYCTwUeC9waNUo57AK6s1jc//Ea2rSmiNg1ImYCT6IKsguA9wHvioiHDeu8b43FDakRjBOAt7RE4/r2+2rDiDMi3gnAo4A3Ur3w62TmP6ONZsQQe10j4kiqPPw08IiIeDXwDuBVUSPnQ2uUR8SzgZd1FGsy1SCZCvyd6lC6KyLWGfGcYZYbhwMbAW9rv+8QNWLOkPers+PVdFr+9rT3nQGcCNwK/BX+Xd8NlAi091grIj7YYp1EdSqd1Cs3hvkZtplWz6aS6Y9k5jcjYirwgYhYeth1ZmZeCbwG+AowBTitPf6vzPzXMGK0smhfYA+qgbwrVVcuA3wWGMrI3QiPB54J7AKcRc1i2yjG4VaFiHgtlYDeBjwUWIFqw70AWKU9Z7xmYW0M7BA1DfgC4CJqRtajYsDOzojYCDgGeA9wJfBx4A5g1YhYYsjXcpftgOOAI6iE6TxgBnBIu8aGnVzPiIijqU7N11J1zOnAL6NmWK40pDidXl9Ro7oJvJpq20Od/++LGqUc2jUWEW8DpmbmfgCZ+X7g88AFgySiD+gkdMRJ+BLge8AjgHdTvTSrUT0MB1In74pDCrt0izcDWJfqwXtpVO8Qw7oI28mxXkR8BzgOeHtmfiNq9PWdEbHGoDGgCt1Wmd5IXRiHAX+PiBWBU4DbMvOeIZ+sOwF/Ae6lpj3cAdxAnbDfAD7UGg0Df5ZRU74AfkL1Fr69JaInMORENCKeSlUGvwOupqb+7gR8pY0q/6s9b+B7GzqONZbjNWhDciPqOt4U+C3VSNgOuB1YjrrWBp6CFhHRRs9WoQrpl7dG5CrAO4FvZ+avhhBnsah7CV/T3vP/qEr7WuD1EfGYNpqxOXBgDGFaf0S8nUrgTwH+DLyVKqeOpBoPL42IhwyjUR4Rz6HKw3GNNeK1STUg51CNk/Wp8+WuiNgi2vS6fuOMinkEsAnwKapBdyhVkW/REqmhJKJdHq8Wb0c6Kn/nEfvRVIPoYCqxf2tEbJrl3vacMddtrWPgiZn5S+pcOLMdrxup8+UDw0que8cg6tafW6mkcOWWaBwPXJqZfxtiZ9k27f/FqPvi/gBsDVwUde/VBRExcxj1WGbeQyUxU4D9qLrlLmBP4BfAsjGkxWhaWXcHVXddB1wBLEl10u3TnjOU2SgRcTCwKjUC+gPgR1SH4BVUw//Y3jk/jHijZeZ3gc9QSfA/qds+PkuNJD5hwDptCrAx1V6/nDpmW1Lt35OipqQPoy01lnJj0HbADKpMDOAqarrqn4CHU7MPh31b2lJUQvhpqp2xKpXQHwfMyMw/te0aqPzt+Po6kWo7HQr8g0p6b2g/P5oaHSUiVh8gRrQE/iDgg5m5S3t8cYDMPI46P87vNxF9QCehrUKZSA2Zf5sawfgGMCczD6E+8E8Dn8nM3w8p7BLAbKoQmwF8LjM3Ax4XEa9r29VLAPo6YUccpD9QyfOBwKSIeCHV23VZZv5moL1o2knxNOAZwO2Z+S2qEXQFcDZwVUScGBFnxRCmQkTE86h9+hR14X8tMw8ENgD+D/ghdbGc3RoNfVcKUVMbToyI17VEe39g+Yg4elQielgMYbGizLyMmgK2D3AzcCHwHWDDXk9aRDyT6vkdRqyvjnesqOkwK1LX0fwcr76nRUbE8sBzqNGRi6kRqOdTlc+9VOK2AfCyiHh8RKzcZ5yRUwJfBbwpM78dEatRFcRnMvOU3nMH2J8JVNm0I7B6Zp7X/pTUCO9VwMsj4rnUaOz3M/Pv/cZrMd8LrE418v9KdUxsRF1vi1Of4/LAHr3EZoBYL6DOgXGPNWIEbS/gS9R1uxzVELqFanC9A1gqahrrw/uNBRAR76Tu1zmTahy8BNiZGvU6F3hSRGw7Ytv6LetPAh5JjXCN62fY4u1B3cs17uXvqLjbtI6YI4GXZOaXqXp0FvCKiHhce97HqGM5lveeBDwO2DwiprQYd1ANyW9Qx+6bwCm9htAg+9KO91Oo0dw3Zebx1DH8VPv/wohYKSKWiLpHr+9RlKhO58Mj4kCqQXwl1e74CVWOXA0cC3w1M28fZL8i4l0RcSyV0CxN3YP3I2qEvtcuWII6N6YMEGeziHg6sDfwXKqd8UlqRHJv4EeZ+d6IeBNDGBmKiPdTCcY3qZHWx1MdV5+hZg9Npq69A9q2DW3K56gG9wHApVTH0gVUmFkAACAASURBVFXAl6kOjN2penvMiWjUlNG3UnXj16nP62jg7JYAfA04LiKWG7AtNdZyo692QEtqXk2dBytSo/3LtjifpTor1h/x/EfHAFNYI+KlUQMV61J1yzbAAZm5EzWr8nHADRGxXkQ8dMD6spPrq8U6nurseCXVoXkQ1dkzhapTfgls09oeB0XEqn2GWpzqiHsO7X74iJiQmf/onfvtPPwC8Ll+6pQHZBLaq/BbL9m+1MX8TmqawKnAlKgRoFnAE6lRlYF7MaJ6aLegCrDnApe3CghqJHbzVun8X+t96uuEbYnh04HXUVPO/kiN2BwP/DIzPxkRV0fEZVFTTPrZl95nuDbVI/1aath8BepCmEP1ui5BXSDfp3pQ+ha10MxS1IV3MlWg/KwlA8tTlesB1EXza+A1gxyzzPwrdfy3jYjXZObdVGNv2Yg4qiWix1MNlkMHaEg+N2rKNJl5FtUQ+QB17C4EJlHTWKCmEU4dINZ+reIhMz9CNbTGK9aLqELr+1SD63rG8XhRBfOTqKlKi1GNhV8Av6GmkHye2r/1qOvw7j7jPL4VkO8BPpyZl7ZC+MPzeG6/n91E4INUx84p1GezU7TpUZl5a2aeSk2nPhF4Y2Z+pb/d+XfM5ahG6Ysy86fAVtS0ordRDf7HUT3kO1IN/f0HODeWphrBXcR6Zmv0v5m6tlYBLqMSjC+0319DlWGPA85p29aXiHgDsAbwLKqxvzvVSLmAut62oBbkeFYrp8fcK98aW0sAv87MF2XdGzZun2GLuSI10noNNb19XMvfUX5GJQGH9zqEW9J0IdUhc3BEfAm4IzPPGMM+RWb+kyoD76DaA6+myom1gf0y88asqbnfoxLRQUeEFqeO06XA79p1tz5VVi1NdW6eDXyEmpk15ni9z719VntTbYG1qA7o66hOwSuoDqDbMvM7Mfi9qLOoMvFC6hw8jSqb/kmdL8+gEsXNM/OmfgK0bbyZarBe1d7/3VQ5uCHVoJ0VNQth+cz8fr87E3Ur1huBH1N14o5t3w5u/z+Eat88merQeiZ1/t/Zb8xR8ZdobbklI+LTVJL4VmqBmA9l5smtDt+BGhGd1keYh1H7dyJ1fW1PXdd/johLqRlENzJY+2a19h7XMI7ttvaah1DtjMOomVDPopKni6gO3UcA60TECq19cgKD3c/7S2o2We/2gLWA6yPixVQ5chvwRapzcLcB4kBdsydS+/J2xuH6gn+3236fma+j2i+rU50RH6VmGkyjOq7WoRLgVenjnG9l791t4Gp7apQcWt2bI+7rbonoH6iBoDF5wCWhUdNHM+reoHOBP2bme6iRhCuo+/0+DayRmXOoimGt3usGiDuZauysSRVYf8jMo9rf3kP1olxAJafPB57dT2U34jVPowqsd1EnynSqkfzoqIU5dmoxP9bP/rTPcDvqXsIXZOYl7f2+RVWe21MJyP6Z+fHMfE9m3tBPrLZfM6nG3DXUhbEW1aicTSUVy1M9RBe25PGnVIIw5mPWksLefbM3t/3aKSIOzsy7qAbrMhHx3jY94njg6D5jnUI1fjdovWUvpqZ/fYkapbyl7eMjI+J0qtfwpD5jvbG9vncf1X7UfS1fHIdY7wdeThXCW1DTN94IfJchHq8RnSFrUNNS3kk1DLagZjdcTXUizaTO1Z8A783MUzLzL/N80/8d7ziqB3wpqmf1H62SPQE4nxo1eULUAhb/ntXQh6dTvcU/jPrKlwOp47F6RLy+bctmbZ/2zswL+4wz0rrAD9u1vQVVhpwBbNRGzk+lkrjPAS8FTh2gTNwG+MWoWB8edqyo+9T3oma1bEn1Hr+VqkhPokbJN6Ma/TOoxssLMvPGfnaqJRgXA89vx/6p7f1Ppe4Rupm6vnejEp0No4+e+PZZPLW9B+P5Gbb3fwPVgLucGmFYi1qI62qGXP6OitvrDb8u657GrSLioKgVUSdkzVD5WtuG32W7n2h+6s7WcfTEiFg1M++kyr/JVBlxLpW4LdPqZzLzaCop7euajohXtPf5B5XQ/oJaROpY4PzM3I6aUv0i4P8yczfgOZn5xzHG6c3U2KaVQ8+i6s1lqXvUXkedg5dRdejnI2LDfkefelobYAI1ZXR9KiE4nkoG1qZmBtwIzI76KqsxJRpR9z1vkzVl+mtU8rU81QjenBoN/QA1ffAxrRE9yODBocDamfk+6jpal0raZrTfp1NJyC+paZ4/pka2/9RnvH+LiC2pZOmhVHl1DXVunAOcnpnvbs87g7kdMb+ez/fec8Svm1KdcYtTK/6eS7V//0y1QW7OmhV4RJ/l7/rUsfkTdf6tTZ1zQ20HwL/LxCdQSdPdLc5kas2LZ1DJ2l7UYMh2VCfd68baDohaM+N5LebXs6ZK705N892aSrKPpsrjp1Mdks8HLhnrPrV4q7S29u+o+utlVGK9FEO8vlqsE9p2vzdqRP9Q4MVU3XknlSg+o+3fNdQ584LM/PNYY7UyasvWjlmNuuVhrRwx5bY9p7emzO3USPqYPKCS0JZI9nbwPODazPxs+/N+VKPkzdRNvs9tj18NnDVAYxKAlrz8nCokj6buAdk/ai5072th7s3Mt2Tm7sAZ/cQc8Zo3Zubl1AVwHjArM99AJdmvAR6emb8bSwXXet/Xbj9vRPXG/DQze4XfVe2xq6iC5RBqkac1x7ofo+IeQSXwH6Yu6r9SFfcFVKXX6xn9JPCXdvF8IPu4Jy/q5vJDqCkNvV6Ym6lG43YRsXc7lm8Bfh/1XUZ/ac8Za6wtqQbdK9pDW7X9fEnr+fkN1Si+hZrmeS6wb2b+rI9YR1CV9RnALu04/oJqVL0b+NUQY21JdUAcQFUAd1CNhk8A/2oxhnK8RiQxp1ALU/yF6gl9GNUzeT01feRu6lr4NVUZjVnUjfNPoiqDv1Of15epHsmvZOZp7f0/DGzSKo6xxuiVmdtQxwvqePw1M/enZjXsHHV/zceBIzPzKwP0Ui8WEXu3Bt6RVOUM9TnOohoo/4paWfMUqqH/tsy8K/v8ipjWA34Yc+uHP1MdId8cZqzWcfVk6tx7AzVScjnVQ70ZVZmuQk01+ivVqH1JZl7dz35BJRiZ+d2s+3RXokZQjqGm7T2hdRY8npqyvV9mvrM1vuZnf0aWvxtTx+sb7c+3Mg6fYYv1TmCzzLw2a7r3b6ly5AKqQ3Bo5e+ouOtTszF603GnUo2VS6ke/xPbU88Ers7Ml7XnLnZ/dWdEPJ5KKnYFntzqs8dS5/+mwCMy83uZ+dp6erymvfTWPvflQOAx7Rh+iCqTLqcaybMz85x2vjyXutewlxCO+WviWpm4GVVHXUJ1ND+XKrP+TjUmLwJWzcxLqWR7vs7B+9i3kdNAV6bKx09Sjf2vUh08xwB/ycxXA8/OzJvGkmhExLupxLLX2faIFudrVFvjd5l5GDXt8l7gm61siz6Tpw8Aj8/M3mrxa1PX2puoMuVHVNtweSoB/QW1MN3At21FxDOopHBxqrPnvLZva1Cf6Y3teWdSK+QelJmnz0/yG3XLysZRUzvPpOrmL1H15dXULTn7UXXPrcw9D8c0VTtqKvljqfL1oVSn33epkfLebSVDKTeirRAftS7CiVTZvho1A2uf9v4Tqc/zs9RA0GXAUf20b6g27mNaMvqxVpYsSX12j6IS7p2ohPqV1DmzV+s8GZOo6fSrUgnmxlT59DCqI3xo11eL9TZghayZNfdS58Y7qNHH9Vr8O6jZGkFNoV6udaqNJU7veD2V6rB9Z3u/M6mZJo8elYj21pTZJ/tYDXrcvoOqH23HJlKN4Y2paRy9ht/PqYJmKlWw9aZHXjpIzKhFAW7KzF6vwZcz82tR98odRyUhJ1GJ2yER8fvMvIJqrI8lztSskdtezN2p3u9vUFNjr4kayXtPRPy07esPxrg7j6Qq7AuohuOBWffC9Qr6IzLzuog4h2qcX08VnHeMMc7I/ToauDszXx31nVh/bft2JlWZHk0Vyntm5m8i4snALX1WPG8BHpmZG0eNoJwXEbtnuSVqNOCFUfdH3BrVQ97vvQs7ZeYXW0E9hapUV6YKzuWpHsjTIuJZ1Hn65sy8uM9Yoz/D26meufMy8w/taR+mVmocNNbI/VqRqoRWoho9d1LThv5E9Z79qt/jFXUP7jOoAnob6jyYTTUItqLufVqMuo5+TPVOrg58us+Epjft5ZnUIl/3AvdGRFKrQX8fIGu12gltn68faxxqcaHbqVHVYyPi/4DfUysj/jwzL4uIH1OJ/CbZRuz6Od/b6/4VNer/Aqoj7EtRU8OnUA3Y7alz/BjgW5n55vZ59Nu425ZqFB+XmVe09/lx1OrjO7RYxwLfGCRWO+c3oEYBv0B1HpxAzTpYgWr8/HtWADW6sW/W1OAxG1X+bk2NYB0QEadRjYbLqI7H06npj0f2sW+jy9+DR5S/P4qanjvs4/Uualrjru33/amRtR2pBGlo5e+ImJOpa/ex1FcsbUKNFH6VuqeWiPgddV/ts6jV5V/VHp+fBPRF1FSyS6nG8UOBVTLzqIjoLTjzjFbOn5yZB7XGe1/XWTsflqUaW29osb7Z/vYN6nuFN8rM70XEz6lZUvf0Ey/qq6KeSX12L6DK4O0y86yI+DLVvtkZ+FRmvrfF+NxY96nFemZmfra3rRGxLzW69Mase7pWBJbIzM9ExAEDXFsnU8ept9r/wdQiiE/NzLuiOvu+GzXDa7nMfEa0qaxjjBPA5KxR8S9Qt91sQdVhJ1CLv6xLdf5cTiVVa1DX92FZ05wHMuL6Oo4aMPg11ZkA1QH5bmDPljBcmHNHe+/3vAfIzD9HfQ/jRVQC++L2+tnUDKKlqbboLdSMvTntdWPpMFiOSlxuoDp/d6cS3TdRdeKFVPl0I5Wc/XqAdsBk6raG51F11ksy8/tR36G5R1bnzkSqvfsJ6hw6L/ucldfagJOpEcLDqNHAXwDLtHrsOcBP2rX8RmoU++B+kqeo+2ifRLVdplOzyf4CHNuO4wrA4oNeXy3WEVTn27va73tSOdL7qc7bKdR1cBdwaGZ+ut9Y1O1M51Ht3FOpNtpR1ADgu4H3R8R+mfnbked1v3XKA24klOopuYEa2XphROyctUR5ZubPqd7/Wxl7gjaveL0ejM2ivgLmL8DdUdOIbqJ6ac6lpq3cSx3o22Bs0/haIfyiqN5cWmV9d0R8IjMvyrpHZj1g7VbQJu27DMciM39HFSK3AQf1GkAjntJLaG6mCrg/UiuD9Tu17XlUAnt0SwLeRFWil1JTBGZThdnyzE10v91nkvEmKpn5VkQ8qjUSf04VXD23UZX7mvAfPTRjjbU4sG/UKPg0qrE8iUqsNwZ2jVo45wyqp+gcqlIYs//xGV4C7B5zV789q8X62ACx5rVfE6np2TtQFeolVIOs18Pf1/HKmkLzdGok4/WZ+TVqBsOZVLL5YWrqRq93+Gftef2cGxtS92a+jup9nBER+7TtuIe53wvaK+9uAF6ZmT/uY7/eQ5VNb6SmDt1DTeM8jFoNd0Oqx3UP+hglGbVfz49auXgParrvElQlcAI1EnQF1WBYjGow9G4f6Deh2ZLqcHsntVrsjMz6kvKs0cdZVDl4PdUDO6ZYUdZsjaDrWpx/UVOkH0Gd32tS13CvEv0VNaK8dz8NhRZ3dPn79bY5H8/ML1L1yabUiOutmXnwWPetve//LH+zpoV9iepVvpE+PsNR+3UslYDu235/KXUdv6zVmWdQswAGLn9HxJxEzRaanpnnU9f0ScDJEbFJ/Od3Wm5OjYDt2V47PwlobzXk5amG5I3UyO5aUSOtl7fk7ID2PKAa733uz5OoxGUFqsG1CXBHRHwyIiZn5rep8/LkqJGBxamkZqxxeufB1lQHwSzqXH8+1SEMVW+eR8282Tfqdoy+tGPwyqgvsSfqNpIXA+/OuSMj11GdalN6DeSxnotRI6AbUp1ym7YEdEdquvtd7WnXU7db/CozD2qPjWm2S/v8NqJug3ohVQ8fQXV6vIS6r3YvqiN1D6rzcxp1XF/Wrs2BtHLkD1Sb45XU7SRfohYGmpiZd1Cdqx+hrrfZ7XXzc96PnI32bur+vkuiVkWe0Npo17X9vLZ1LpyZmWNuC2TmrVSH+hZUAvpLalRwS6ocPI1KspenppdC/+2Au6i27erAG1oC+giqnd/7yrLlqfbNClSnQV+L90VNY1+Tmq7/dep8D+o4PLU97VZqoZ5HtvL4uX0moI+gEuc1qc/wVip/WIW5X/32W+r6Wqnf66u95kgq2T2TWpjveOpWqqta7KWoDtvegNy17XX95neLUWXcGVR593jq3HtT+9uPqa+AWWusHUn3FWyBi4jVoxaAOI3qATygNbA+SRU6O7Xn7UUVeH+nhrsHidnrFfwC1bCaSfVSfzfbvRft/3uAdVohdhjVqzJWv6Ia21tExLT23q8EboyIz7YC9m5gQjtBX9sS7rHu09OoE+XgzPxB74TvnfQjTpiLmZtMj7nyjrrfZx/q/Dmamj63DjWNeAPqQvwmVQj8jBrteH9LgvppbL2dmmJzEVVg7RoRa2fmodSqwqe1ff09tRrdU0cl32OJtQPV6fB8amXVFakCeRI1gnc31eO5PnBJZh6fmZ/Isd8XdH+fYW/lxz2iRqe+0mKdP9ZY87FfW1KNgqQKsB9T8//7Ol4t3mZUp80VQEb1up8B/C0zP5yZc1rF+h2qIv9GjnHl2IjYujW812vnAu09pgMvaNf4yPO+N1pyS2aO6TqOiKdExLGtAXQzVfAfSB2jQ6jOrCOpBT9+Q039uuu+3m8+4p1ONTgmU2XdTVQDZxXg5y3BIDOvbX//NnMXDBhrJbd8K2MfQzUej6Mayc+J6gTszSZ4GnU99xpXY61Ql6NG8w+hGgqTqfN8N2ra0i+oxOI51Pn4U+CGrEWexjydfoR5lb+vAG6KiHMz80vUZ/gzqkOh13Ac6+f4P8vfFvfHVL12VXtNvwnoflS91EtAd6dGi19ELbzRu2f+HGqK2ymDXM8jtv+fVAPyoIj4DNUZ8jGq4f/ynPudlndRPeerxNxpl/fXEF+RKle/QzXw7qFGlJ/O3K9OW7YlwksBj40Bvh88ajRmVep6XZ9qPN5DnZdbUTMdFs/Mz1CJxyrUdf2H+3jL+9Q6c55MJbnHUOXFy9vPB0fEVzLz+5l5ZdbK+C+m6rJ+9qv3We9ErWUwiyr3n5M1E6r39QpfpzqxHjlyO8cQ583Aapm5BdWmeAVVJh6U/zm18VtUG2D09s23tl1/pDo2XkJ1Xm1Indt/av8+QHXCPLHFOxk4PvtYW2C0qHt330KVw1tRydlfqPJsuZw7Mv4P6vx9HZUI3e+ARdTtD6+LiI0i4iPU53UKlWhvRM08ITN7K+4+qb2unzVJXhB1K8Cp1FTiX1PX8yHU+XYcNdL7J+qz7avd1mt/tY6b6cCrMvNbUeszfJBauOnDbb8uau9/KPC+rPvIx7pfu1JtmbdS5+Hd7f3uoD7P3tTRr1LtyO3b53dPH7EeTs3GuJVKnP/W4jyBts5KREzLuu///PZcWvyxfo7voxLzD1EzrmZQ9fDJVB3yeyoRXpm5qzLf0mKNdabB8lGzWt5AdaRcTyW5QQ0Y3EwNuLyDNl06hvF1en3Uf0PXKp9pwIqZ+dERjy9JNZCfQZ1UW1BTsn40hJhbUxXnmVTh1muEHZeZv2sX0Yuok+whmfm2+enRGhXj38+Pmk+/E9Wz8K3M/E57/HjqJN2L6jH6AfCvPk7WlakVuC7OzA+Ojj+P50/sFZxjFTU6NxO4IDO/1CrXzaiLfS9qxOaj1MWTWVONlu8z4T2MGhns9bBOoJKJh7XHf0wlVBtQhee9wJI5n/dxjYq1Wtuvq6kC7SFUhfYQqmFyO3Bm1j1+7we+npnn99m7Nb+f4erAYlmr8vbbkzbW/bqYWnSnr8o7ahGiD1IJ9S3t/zWoCnQ7Kul+MtWw/Ga2qW99xHk4lcDcCXx0ZBIb9Z16lwHvzMxzW8L/OGoqcz8rxa1GlVEX9uJExCXUfXdnUPfSvIcaxVsjM9/Vzz61930s1Tn2VarX+8SoqehPoTrg1qXOjW2YW/Gcl318hUMrY/ekKrbjsnqHe9OotqamDn6KKivvycyXRk2l62vl4nZcnk8lmzdSSXzv3pN3U2XhIVRZ/zeqUdLvfX7zU/6eQF1jz6HOze9mHysX9lH+LtnPeTji9cv2GmtR01dfBnw+M4+OWhhrKvUZnkE1Jv/cT+NuVMxJLQklasrxqtTMg4uoaWGHZeaBUfdJPZsa3bg953NBnXaNvY4qG35KNfifQF3bZ0TdIrMlNbPh41S52O+5MYFqMG5K1StLU3XLxlTD/ENUufhkqnPwj8A7so+RpxbvaVQn9l5UMvuKtq8vpD7H3vdp7tlvvTwq3oTMvLcl7KdT96wfMOpvz6eSureM9dyImlr+lKwZLr0k7dVUffJJ5o5mrUclNL+lFtEb5N7Wp1Bl7LuoKcvrZ+aToqZYbkIloStRn/PMzBx4tlyLO506N9ajOkRupc7P7albw9akpuYuQ61a2s+9hS+nbs+aRSWCH6I6uU+Omor5z8x8e3tuv22pqVTZejVVb2xKnXsbU50R51KjXntTU873jvrKjX7P+SdS7evnZuZPo2Yenk/derR1e86krFtkplDX85hn5bVzfFWqbN+fStR632Lwcer4vJD6PA+PiHWp26n6WSdkMaqdtinV4XETVU+tTXU+fAHYhZqa+9b2tz9lH1/N1nKUF2TmPu33k6lj9QGq7fYQKj/ZkOrU/SLVmT/mb7mIWmBr17Yf36PKxNupTsDrqDbW3lTnzg+p9vb5/Ryv/4r9QEhCe9pJ+kKqZ+bunPvl1sdSjaFn5gDzqkfE6S2A1PvKl2OpqZ3bUT0jl2XmL6LuF3oftbhPv42uoOaF3x015WIHqqD+StYc9d69DBdS8+/7HXl6KDV9+F/U9xFe3s/7jDHmdlSF+gdqZHV/qkG5KdXztD1VcB86wH5tRjVudoxa7OM51DScTaiFG86LGindm+rJ27ffWKPi7kBNb7iEmgJ7PdUQX4baz12o75AddHGP+/sMd6CSjr4/w1Hx/td+vZzar88Osl8tObyAumbXob565eDM/EhrvE6gevC+BfR6/vuN1WtQ7QCsmXO//3MxqtfubKqB+TaqQL+y31jtfY+gjtNPqIbk0lTl82NqhPJbg7z/iDgbUJ1ge1K94k9kbg/5LKqs2oRKAm7OMY7sjorVW63zCe29rhvxt6dRn+P5Ofe+vn5H7xajzodtqU6WR1A9uq+hyt2NqHL4euq872uhg9H7xvyVv7Oy3X/VZ5zOy98W92VUg+F0aqGWo6JuLTmM+oyPpxbEGDQBnZqZc1qD7yxq9PC7VBm1FZXIL5GZO0Qt8vM8YMexdIy0955KjQLeSjX4L6Cut6upMn6J9tgv+uksGBVvCar+n0jddzqBOn53UA2tlajOmZ9QtwrM6TPOJGoxmUuoc293aur8PsC6rWPnMGr/ZvWSxUGNSkQ/R3VovaH9bQ8qaRzGNfY66rp6aXvPO5nb+H8Itd939tth0NsXKrl4LFXm9u5d732l12XA9Zn51YjYmboGh3EP6BIt5hLUCNGfqPLppdTMjU9TZdq91OyNX1GjfGOdbnwalUhdQ9X9n83ME9rfPkYlVJdnu4d8wH2aSHWo/o259+B/kerY/DjVHrk2M184YJxtqNmNj6VmwZxMzaL5C1X2H99PMn0fsRajOr7vphKn31BtjOWpevogagr197NWUx4k1jLUuf1SKlHbmGoDXE6NGu5JteE+D7z6vjoi5yPOElTnw79aR8uh1HT9z1Ln3oZUp8yV7bGfDlImtngrUrdjPZpKSj/ffj+AOr/fS3VUfDT7uJ1pnjLzAfGPKkwOoqbwrAuc2x7fgzq4645DzD2oi+Jj1CjDOtRUj9dSI2+XAFMHjPEoqmdw2fb72lRl80qq1+SY9vjyQ9if3kquM4EnjfPxWrZ9fidQi80c2D7Dw6iVyIKa3rTyEGJtSU2XW4xKJvYc8bf3Uve5ADxsSOfhZsAn2++vpQron1IrGH6NGula2D7Dzvardyyoiua8duwOoaa0XUUt4z/Mc7GXWJ9CLUrUe3wqdd/hVdTXYgwaZzLV+NmYGiHZvz1+eitDXjHk/VqfaiS/nmq4Htce/wS1QMew4gTV2L6KGrE+YMTfPgqcPPK5QzgPV2o/f4nqOHo3dV/hEVRC8wXg0UPat/srf49ujy9U5W+L90Sq8bEq1cj7CjUtaw9qyv0SQ4rzLioR24oaOeudh9NHlCcHUw2xZ7dyZf0+Yz22bfubqZFdqI7g/YC1hvz5LQlsMSLGX6lOzje38uS6Vn6tOoRYk9r/+1CN/KOoUX6oTvBj28/LDnkfJ7T/J1OdV4dSydxQ2lTt3Pta79hQDfOhloOj4m1PJdRPaWXTD6mZNSe28mTg67jF6Q3QbEjNKNiZauy/usW/nBrVu5yaUr3RyOM8xlgPpzpIaeffR6gpvlCjXsdT5eYwyqil2r58lBo5fF57/Ih2HI+iOg4GPufb+67fzpGLaO02qgw+kGrLDaUeozqQevd2f4y6Neb51Gyix1P19aXDKkOoJPSwFudaKunchiqPj2z7+PAhxVqCagP8f3tnHmNXVcfxz4EWKG1KoIwsolBMBCVCWWWpZd9aKIIsgkKKMYBb1GqUNCTUGGIRElBEEBRKK7SAYEyLJIBSChWEsAkVmxhoIRQIi1JogRb8+cf3PHo7vJl23rv3zsyb7ydp5nXem/O9795zzz2/81vOufnevSrfz1eT7YeSdDZCixN/Qc+y89DC9yO5D34jf25YWZoRMeA8ocVwn9+jm/wnwFmh6rVlam2LHjxT0ENhGrr5n0eTzMPQSk2fczObaB2CXPOnhKq4fhJ11gdDuUm9hm71UWsMmmQFCn/ps2u+j3oNQWdqBwAACvNJREFUr/I8ZBA+Hu3lb/WkMwFNHJ9BXoy/pZSuQTmGU4vHUoLWxmiC8i4KBR+Grt8eaLI1r4x+UdCr6xzW/b2OQYPZY2jyuil6kDbCilryqDXR2Qat0q1A48X7aNX1IGQQnBWtlXrvTXMcMCVUmfMa5BWfX6ZG1tkT9fvFaIJzce4np0cbBWaa6IxCE4LXUko3o/DYw4HVkVfEy7q/Cpoz0ULFg6EwqRuQd/eLZV6vTh1/8zXbKLK3MXvpF6PFrYVlnMOU0nS0ODsbeYKmRcHbnz0481Cxr5vRhOXCaL3S6mgUBfVe0jYcy9Ak/b6IuD1/ppRxo4n2NLQoPREZ00tRdNKyEjWGoXF3JjKgRiBD8fz8fqn3WG6zOK96BHmWvxNtekAL7X8Ynp89rLuH9q4snXx/rURRNhch79e7KJ+7C6WRtLzfeTet8WiBYEv0HHkMLQrC2pz2Rlj3W2guSbt9M2m/2sa+py+EtvUojew13DQi3knaV/siNGYcERG/LVMr6+0D7BY5pSj/bgoyuM+IFtJIetEaj6IyxqLrMQlFec2g3D5fvKcuQ6H076J0jovK0Oim14UWol9G9sr+qG80IhtKGxPzc2U1isTaDS16b468sveVPf4OKCMU1gkNux7lX82pUKs4eJ6EVrkujYj5qY2cyR60JiBvUMOtvlnkSnGlX1RV7roOuDkiZpbVbg9ajet1Kap+d1WFWvuhh87RKJ9mk6rOYW5zCxTucB2wJNoM/+pFp7ZzmPXq+l4bRyEfLKV0CvJWX1vF9coaY1CeYWOPxNllG6BZpwt52Gagku/P5d9X0Q/3RJ6gJ1Cu0JNo+4NSQpma6J2CJkG3RA5pq2CMGoZCv/+MxsWRETE1pbRNlJBn0kSvI8ffbroz0WRhFnB3tJhCUmjv12hl/I/o+5yG+sUlkfc8TCn9AC0APYUiVq6MFnPImuhvhiZb49C+3G2FFPeiU8wdnoqioZ5AWzuVZoB209wK7Wd4ZEQ09patZEwsaE5i7aJcyykQPbTdeIb9HNW0OL/M9rtpDUfGxSRUkG0scFOUFB6Yw0iPQmGxX0MG2u9QuspS5KQYiRZYE0oheKld/eL1z8b8mcirvLSixYkRKMphUXGeXYVWoe1GiPjhwAcRsaDk9ovn8AK0wL4lqobfdipfL1q/ACZHxNju75Wo10VOc0B5po1c0Sq0RqJFx2dQJNtctOVj6ePhgKiOW6RwMm9EW5ZsWqFW8SE9Hj1IJ6WURpVpgGathShs5C3WnQD1uQrjBmi9hIy1Q5P2a6yMwrHfDxyRVGSqKq2HkVfocWTMVGaAZr03UU7Dl8hV1FJqreruenRqO4dZr67vVTRAj0PbmtyT36tqsrUCrVhfFxEXVGGAAmTD/TJgXJUGaNZ6HF2vI5F399CqDNDMCygfZBhUNka9D8yPiFWhysYfpJTGVmGAZr2OHH9B/S7fvzchg3FhCQbodGT8XYz6w1zUJ94ARqeUDsxeh1eQFwCUJ1SKAQofbu+wLWsr5VZCtwn3Diiv74dVGaBZ8w0UzjczpTS6in5YJBs1FwLnlG2Awjrj+SpkGFZGRKwJVSy+N+uNRnmobVF4Bh6Fihz+JiKezePv11H446dC1bQPQh7z0Siiom0DOBvxKb+egwrCvZ3/X7pRGCqMNgeNU2MKv6/EAM1tN+YE95ZtgOb2I3t6CUVcPYaMp1IN0IJW43p9F5iVUhpT1b2c5xyzUZrgXVDpnGMlGvOPR9FJ51a2IFfhuNc2SeXRKw0nzTqTgaMi4ttVrgJlrV2jjT2D+qCzOVoFeqRsg7oHvYRWBl+v8vxlrcNR3tpJNVyvkehGvL3qvljzOazte2W9nVDYWVuFnDZAZzjKN5kV2VtToValfa+J3v7Asigp3Gw9WjsCL7drzGyATqXenyZ6HTn+Zs3h6Jne1v2cUvoZqiT9V1TdehyamN+IihLtg/KTbkCVZH8UEae2o9nLseyGUi4qMwi76Z0APBktFiFqQW+daJGKNEag4l+3VTEZL+hsjKr/LqjjeqWUtow2ih310OZ4lKv4KFrQXIJSBlagKIATUM7yJiiFZWq0UA13PcdQ25iYx4wDkSFd+TygLmo+h3XPA7ZGc6lKFmy7aR2AxsOW9m7dII2BbITWRQ77WR3Ky6ulQ9WhU/fNUSepsEVBDVqlhmYPFPy9zFDF429zkkL1r0ZFjh5AOXBboZzPeShH85sop/b4/P5DUVFIf93UvShSF3WNiYOxzzdIqkh+PQqrfDGlNAdV3r0S6ApVnt4FRRyciLa9KdUI7g8G8zUz1VJH32h7o9FOIAqbytd1M9ah08kDS10GaNbqSIPG38sMVTz+Nici3kwpnY/yZrdHHp/b0LYUe6HKmouQV/QulNfVEQYoVJom0K/UNSYOxj5fYAWaE++N+vsVqJL7tYVQ3cbejKs7wQCFQX/NTIXU4pDr0DHXGGOMMS2QUtoBmIxy3m5FWxJMRQVa3kM5cdOLC7jGDHZSSp9DRbhmAK8B05HHf2XOARyFtph6tv+O0pjOwUaoMcYYY9Yh541vgTyg56GqzPNRcafncvEKYzqKgiF6C6oQvswhq8ZUw4CrjmuMMcaY/iUbmV2oCNG9aK/VicCrNkBNpxIRT6E9Ek8GDsm/swFqTAXYE2qMMcaYj5BS2h5Vyj0X7bO6tJOqaBrTE7ky852o+vPc/j4eYzoRFyYyxhhjzEeIiOXA8rwtQNvbvxgzWIiIxSmliZSwB6kxpjn2hBpjjDGmR7ztkTHGmLKxEWqMMcYYY4wxpjZcmMgYY4wxxhhjTG3YCDXGGGOMMcYYUxs2Qo0xxhhjjDHG1IaNUGOMMaZFUkqX9/cxGGOMMYMNFyYyxhhjjDHGGFMb3ifUGGPMkCClNB34PLA58CrwZeAe4E/A2RGxe/7cFcA44L/AWcBEoCsiLk8pnQ7sEBGX5M8uiIhD8uspwB7537bAqcBi4Fe5vTVZ8xXgGuDT+ThOi4gPmhxvs/aeAWYDOwKvAScDdwPLgW3ynz4M/BSYBXwMeCoivtX6mTPGGGPKxeG4xhhjhhL3R8TByBA8AdgOiIIBehywWUR8AbgN+DEwDzgs//3RwB96aX/f/JkZwGTgeGBYRBwEXArsnXWH5+N4HpjUh/bGAHcABwMrgL3y56YBn8ht7w+cAzwdEROA7VJKu6/3zBhjjDE1YSPUGGPMUOLR/PMfwE7Am8AvC+9/Fvh7fv0Q8JmIWAG8l1IaBWwdEc/10v6ciFiDjMtNgF2RZ5KImA/cCewCHJBSWgBMYK0Hc0PaWwMcB9wK7AyMyG0vBZZHxNtAyhonZo2dgY/3omGMMcbUio1QY4wxQ4n98s89gX8DqyLif4X3FyNPIvnn4vx6PjAVWLSe9ld2+/+/kDeTlNJXUJjsEmBuDuP9HvDPPrR3EvB0/vliL3+3BLg8a1yAjFhjjDFmQOCcUGOMMUOJfbN38GVkWH6/+GZE3JFSOial9ADwH5QTCsobXUo2KPvAPODYlNJCYBVwJvA6MCmldB8QwFf70N4i4BaUp/oOPXs4rwWuTymdjcJ2z+jjcRtjjDGV4eq4xhhjhgS5MNGCiFjQz4dijDHGDGlshBpjjDH9TEppLqqAW+TYiHinP47HGGOMqRIbocYYY4wxxhhjasOFiYwxxhhjjDHG1IaNUGOMMcYYY4wxtWEj1BhjjDHGGGNMbdgINcYYY4wxxhhTGzZCjTHGGGOMMcbUxv8BiYFAS7BdVwgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#groupby之后即生成所需图\n",
    "#使用agg对groupby之后的结果列生成新列名，aggfunc对应使用的聚合函数名称\n",
    "df.groupby('province_name').agg(Num=pd.NamedAgg(\n",
    "    column='province_name', aggfunc='count')).reset_index().sort_values(\n",
    "        by='Num', ascending=False).plot(x='province_name',\n",
    "                                        y='Num',\n",
    "                                        kind='bar',\n",
    "                                        figsize=(16, 6),\n",
    "                                        rot=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>level_name</th>\n",
       "      <th>专科（高职）</th>\n",
       "      <th>普通本科</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>province_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>上海</th>\n",
       "      <td>20.00</td>\n",
       "      <td>45.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>云南</th>\n",
       "      <td>50.00</td>\n",
       "      <td>32.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>内蒙古</th>\n",
       "      <td>37.00</td>\n",
       "      <td>19.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京</th>\n",
       "      <td>51.00</td>\n",
       "      <td>120.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>吉林</th>\n",
       "      <td>29.00</td>\n",
       "      <td>41.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "level_name     专科（高职）   普通本科\n",
       "province_name               \n",
       "上海              20.00  45.00\n",
       "云南              50.00  32.00\n",
       "内蒙古             37.00  19.00\n",
       "北京              51.00 120.00\n",
       "吉林              29.00  41.00"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_level = df.groupby([\n",
    "    'province_name', 'level_name'\n",
    "]).agg(Num=pd.NamedAgg(column='province_name', aggfunc='count')).sort_values(\n",
    "    by='Num', ascending=False)\n",
    "\n",
    "#行转列方式一\n",
    "#df_leve2 = df_level.unstack()\n",
    "#行转列方式二\n",
    "df_level2 = df_level.pivot_table(index = 'province_name', columns = 'level_name', values='Num')\n",
    "df_level2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 柱状图：查看人气值前30学校"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xac08b38>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['school_name',\n",
    "    'view_total']].sort_values(by='view_total',\n",
    "                               ascending=False).head(30).plot(x='school_name',\n",
    "                                                              y='view_total',\n",
    "                                                              kind='bar',\n",
    "                                                              figsize=(16, 6),\n",
    "                                                              rot=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 堆叠柱状图：各省不同层次学校占比 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xacd7400>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#stacked = True 堆叠\n",
    "#堆叠图前对df进行处理，需要堆叠的字段转为列\n",
    "df_level2.sort_values(by='普通本科', ascending=False).plot(y=['普通本科', '专科（高职）'],\n",
    "                                                       kind='bar',\n",
    "                                                       figsize=(16, 6),\n",
    "                                                       rot=45,\n",
    "                                                       stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>province_name</th>\n",
       "      <th>mb_num</th>\n",
       "      <th>total_num</th>\n",
       "      <th>rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>江苏</td>\n",
       "      <td>48</td>\n",
       "      <td>177</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>广东</td>\n",
       "      <td>48</td>\n",
       "      <td>162</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>四川</td>\n",
       "      <td>47</td>\n",
       "      <td>134</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>北京</td>\n",
       "      <td>46</td>\n",
       "      <td>171</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>湖北</td>\n",
       "      <td>42</td>\n",
       "      <td>134</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  province_name  mb_num  total_num  rate\n",
       "0            江苏      48        177  0.27\n",
       "1            广东      48        162  0.30\n",
       "2            四川      47        134  0.35\n",
       "3            北京      46        171  0.27\n",
       "4            湖北      42        134  0.31"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mb1 = df[df['nature_name'] == '民办'].groupby([\n",
    "    'province_name'\n",
    "]).agg(Num=pd.NamedAgg(column='province_name', aggfunc='count')).sort_values(\n",
    "    by='Num', ascending=False)\n",
    "df_province = df.groupby('province_name').agg(Num=pd.NamedAgg(\n",
    "    column='province_name', aggfunc='count')).reset_index().sort_values(\n",
    "        by='Num', ascending=False)\n",
    "df_mb1 = df_mb1.merge(df_province, on='province_name')\n",
    "df_mb1.columns = ['province_name', 'mb_num', 'total_num']\n",
    "df_mb1['rate'] = round(df_mb1['mb_num'] / df_mb1['total_num'], 2)\n",
    "df_mb1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 双Y轴组合图： 各省民办学校占比情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xaf93438>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#组合图，原理上是使用ax参数，调用matplolib对象，双Y轴使用secondary_y参数来实现\n",
    "ax1 = df_mb1[['province_name', 'rate']].plot(x='province_name',\n",
    "                                 secondary_y=['rate'],\n",
    "                                 kind='line',\n",
    "                                 figsize=(16, 8),\n",
    "                                 color='red',\n",
    "                                 rot=45)\n",
    "df_mb1.plot(x='province_name', y=['mb_num','total_num'],kind='bar', ax=ax1, figsize=(16, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 饼图：专业类占比情况 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc456a90>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('type_name')['type_name'].count().sort_values(ascending=False).plot(\n",
    "    kind='pie', figsize=(16, 6))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
